Post on 16-Apr-2017
Genome sharing projects around the world
– and how you find data for your research
Fiona Nielsen, October 2015
Find me on twitter: @glyn_dk
• In case my talk will be boring…
First the take home messages…
Do not forget: By 2025 genome research will produce as much data
as Twitter /YouTube.
You do not have enough statistical power to interpret
your data
ButYou can
improve your study design
AndYou can access more data from public genome
data repositories
As you all know…
Data output is going up
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
400K
Genomes Sequenced
The output of human genome sequencing data is growing at exponential rates
Estimated number of human genomes sequenced in 2015
Population scale genome sequencing projects
Population scale genome sequencing projects have been launched all over the world
Soon every research lab and every genetic clinic will have a DNA sequencer
How much data do you need to publish a paper?
2001: 1 human genome
2012: 1000 Genomes (1092 genomes, since increased to ~2500)
2015: UK10K, Icelandic population (2,636 + 100k imputed), Cancer genome atlas ~11,000 genomesExac consortium 65,000 exomes
?
Statistically speaking, you still need 10s of thousands of samples for validation
The more severe the phenotype and the more complete penetrance, the easier it will be for you to find your variant, but
“As the genetic complexity of the disease increases (for example, reduced penetrance and increased locus heterogeneity), issues of statistical power quickly become paramount.” http://
www.nature.com/nrg/journal/v15/n5/full/nrg3706.html
But I am just looking at this one disease…
What can I do?
PRO TIP: involve a statistician early on in your study design!
How can I determine significance?
“One potentially powerful approach is to assess conservation across and within multiple species as whole-genome sequence data become more abundant.”
Look at extreme phenotypes “Sampling cases or controls from the extremes of an appropriate quantitative distribution can often increase power”
Look at non-SNP variants, they are more likely to have functional effects
- “how to account for the technical features of sequencing, such as incomplete sequencing and biased coverage over the genome?”
Think of how you can provide evidence that your result is not just a local technical variation or sampling bias
e.g. data from same cell type, same seq technology, same alignment…
How to account for bias?
PRO TIP: include more reference data in your analysis
• Know what data is available in your lab, your dept, your org
• Survey from Qiagen showed that one of the main reasons researchers collaborate is to get access to data!
How can I access more data for my research?
How can I find collaborators?
PRO TIP: Search for collaborators who have the data you need
PRO TIP: Tell your colleagues and peers what type of data you have in your lab
Where can I access data?
public repositories• some you apply for access,
especially if data contains clinical info or whole genome PID
• some are open access: GEO, SRA, PGP, OpenSNP, GigaDB, …
• some are consented for general research use, some have specific consent
It may be confusing
And it takes time
Bottlenecks: • Finding relevant and usable
data• Getting authorisation to
access data• Formatting data• Storing and moving data
We studied the problem by qualitative interviews followed by a survey of researchers in
human genetics
And it takes time
T. A. van Schaik et alThe need to redefine genomic data sharing: a focus on data
accessibility, Applied & Translational Genomics, 2014
10.1016/j.atg.2014.09.013
Researchers spend months to find and access genomic data, and often choose to not access
data at all
Barriers to access
Barriers to access
NIH / eRA Commons login
No
Yes
Organisation registered with eRA
Organisation has DUNS number
No
No
Write research proposal
Yes+ 2-3 days
+ 1-2 weeks
+ 1 week
Yes
Submit proposal
+ days to weeks
Access granted
Variable: fromweeks to months
dbGaP Application Process
Science…
Find/Download/Decrypt data
+ 1-2 days
Why the barrier?
• Benefits: strict governance, review of consent, applicant signs for full responsibility for governance
• Disadvantages: No control of data once access is given, high barrier for access – too high?
• Start planning your data needs early in your project• When you find the data you need, start application• Use Open Access data
How can I save time?
PRO Tip: If you use human genomic data, apply for the GRU datasets in dbGaP, one application – access to all the GRU datasets
• Some data is Open Access requires specific consent
• OpenSNP.org (Bastian)• Personal Genomes Projects• Individuals who put their genomes online, e.g. Manuel Corpas
and his family “the Corpasome”
• http://manuelcorpas.com/about/
Not all data is restricted
• Some data is Open Access requires specific consent
• Individuals who put their genomes online, e.g. Manuel Corpas and his family “the Corpasome”
• http://manuelcorpas.com/about/
• OpenSNP.org (Bastian)• Personal Genomes Projects
Not all data is restricted
Personal Genome ProjectPGP Harvard PGP Canada PGP UK Genom Austria
Host institution Harvard Medical School Boston
SickKids Toronto University College London CeMM Research Center for Molecular Medicine
Principal Investigator George Church Steven Scherer Stephan Beck Christoph Bock &Giulio Superti-Furga
Launch year 2005 2012 2013 2014Geographic scope USA, mainly Boston Canada United Kingdom Mainly Austria
Enrollment eligibility At least 18 years old, able to make an informed decision, perfect score in the PGP enrollment exam, certain vulnerable groups excluded
Data Generated Whole genome sequencing, upload of additional data possible
Mainly whole genome sequencing
Whole genome sequencing, DNA methylome sequencing, RNA transcriptome sequencing
Mainly whole genome sequencing
Number of genomes 100s 10s 10s 10sData access
http://personalgenomes.org/harvard/data http://genomaustria.at/unser-genom/#genome-der-pionierinnen
Project funding Discretional funds and corporate sponsoring
Institutional startup funds Discretional funds and corporate sponsoring
Institutional startup funds
Areas of emphasis Integration with phenotypic data, collaboration with other personal omics initiatives
Genome donations, synergy with massive-scale clinical genome sequencing projects
Genomes and society, genetic literacy, school projects, education
Website http://personalgenomes.org/harvard/ http://personalgenomes.org/canada/ http://personalgenomes.org/uk/ http://genomaustria.at/
Summary of data access barriers
Data is uploaded to repository
Data is discovered by potential user
Data is accessed by potential user
Where is the data?
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
≈ 5K
Genomes Available
400K
Genomes Sequenced
Only a fraction of the data is findable or available through public repositories
• “even when researchers are authorised to share data they report reluctance to do so because of the amount of effort required“ http://www.sciencedirect.com/science/article/pii/S2212066114000386
• “Clinical geneticists cited a lack of time because their main priority is diagnosing patients. Industrial researchers cited a lack of time because of the pressure to meet the deadlines in their job. Researchers in academia cited both a concern about the potential loss of future publications once unpublished data is shared, and the lack of time and incentive to share data as this does not contribute to their publication record. Researchers from all categories felt that they lacked sufficient resources to make their data available.”
The barrier of making data available
But I do not want to share my data
• If you expect data to be available to you – you have to make your data available too!
• Encourage collaborations: power by numbers
1. Get credit – publish and make your data available2. Give credit – cite data sources3. Understand consent – for all uses of clinical data
Best practices
• Use all available tools to make your life easier: • Data publications visibility and citations for your data, e.g.
GigaScience
• Figshare, Zenodo, Dryad for sharing open access data
• PhenomeCentral, Matchmaker exchange for rare disease research
• Repositive for finding data across repositories and make your own data discoverable
Best practices: use the tools
Does #OpenScience matter at
proposal evaluation?Based on: Winning Horizon 2020 with Open Science,
http://dx.doi.org/10.5281/zenodo.12247
“Weakness: Involvement of non-academic beneficiaries is limited”
“Weakness: highly focused on academic activities, and lacks an advanced communication strategy”
“Weakness: limited exposure to non-academic partners & infrastructures”
Excellence
Impact
Implementation
“data accessibility is unclear!”
“data storage & access not considered”
“Strengths: extensive dissemination of data to the scientific community (open access, databases)”
“outreach activities to a broad audience”
“research software is freely available”
Impact:
Make the (research) world a better place by sharing in return
Best practices
• Digital consent: towards automatic processing of applications
• Dynamic consent and power to the patient, e.g. PatientsKnowBest
• Privacy-preserving access to datasets: preserving control and governance with data custodian, lower barrier for access
What the future holds
In the meantime: It is a jungle out there!
What if finding data was as easy as finding a book on Amazon, book a hotel on Expedia?
The Repositive vision
Enabling efficient data
accessIncentivising
best practices
Trusted broker for data
exchange
Repositive is a web platform
Discover new data sources
We are indexing all the public sources of data, so users have an easy portal for searching through data descriptions.
EASY SEARCH
Repositive is a web platform
Make your data visible
As a two-sided marketplace, the users can also make their own data findable.
SHARE KNOWLEDGE
Active Repositive users increase benefits
Build a data community
BUILDTRUST
Users can interact to find relevant collaborators for their research either to analyse their data or to combine data sources.
Active Repositive users increase benefits
Find data collaborators
SAVE TIME
Feedback from other users through ratings and comments helps users evaluate data quality
Benefit for both sides
Data consumers Data producers
Find relevant data faster
Feedback from other users through ratings and comments to evaluate data quality
Find collaborators with data
Make your data visible
Build credibility as a trusted provider of quality data
Find collaborators to analyse your data
Best practices - recap
• Get credit – publish data• Give credit – cite data• Understand consent
Thank you!